Overview

Dataset statistics

Number of variables49
Number of observations9224587
Missing cells44304565
Missing cells (%)9.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 GiB
Average record size in memory400.0 B

Variable types

Numeric15
Categorical29
Boolean5

Alerts

ORIGIN_AIRPORT_CODE has a high cardinality: 657 distinct values High cardinality
DESTINATION_AIRPORT_CODE has a high cardinality: 679 distinct values High cardinality
FLIGHT_DATE_LOCAL has a high cardinality: 727 distinct values High cardinality
TIME_DEPARTURE_LOCAL_TIME has a high cardinality: 1051 distinct values High cardinality
AIRCRAFT_TYPE has a high cardinality: 65 distinct values High cardinality
FARE_BASIS has a high cardinality: 33211 distinct values High cardinality
UPGRADE_SALES_DATE has a high cardinality: 481 distinct values High cardinality
FORM_OF_PAYMENT has a high cardinality: 79 distinct values High cardinality
LOYAL_CUSTOMER_DATE_OF_BIRTH has a high cardinality: 24920 distinct values High cardinality
LOYAL_CUSTOMER_REGISTERED_DATE has a high cardinality: 8930 distinct values High cardinality
SALES_DATE has a high cardinality: 365 distinct values High cardinality
INTINERARY has a high cardinality: 55794 distinct values High cardinality
BOOKING_ORIGIN_AIRPORT has a high cardinality: 145 distinct values High cardinality
BOOKING_ORIGIN_COUNTRY_CODE has a high cardinality: 51 distinct values High cardinality
BOOKING_DEPARTURE_TIME_UTC has a high cardinality: 83698 distinct values High cardinality
BOOKING_DESTINATION_AIRPORT has a high cardinality: 147 distinct values High cardinality
BOOKING_ARRIVAL_TIME_UTC has a high cardinality: 86418 distinct values High cardinality
MARKETING_CARRIER is highly correlated with OPERATIONAL_CARRIERHigh correlation
OPERATIONAL_CARRIER is highly correlated with MARKETING_CARRIERHigh correlation
STAY_LENGTH_D is highly correlated with SEGMENTSHigh correlation
FLIGHT_COUPONS is highly correlated with PAX_NHigh correlation
SEGMENTS is highly correlated with STAY_LENGTH_DHigh correlation
PAX_N is highly correlated with FLIGHT_COUPONSHigh correlation
MARKETING_CARRIER is highly correlated with OPERATIONAL_CARRIERHigh correlation
OPERATIONAL_CARRIER is highly correlated with MARKETING_CARRIERHigh correlation
STAY_LENGTH_D is highly correlated with SEGMENTSHigh correlation
FLIGHT_COUPONS is highly correlated with PAX_NHigh correlation
SEGMENTS is highly correlated with STAY_LENGTH_DHigh correlation
PAX_N is highly correlated with FLIGHT_COUPONSHigh correlation
MARKETING_CARRIER is highly correlated with OPERATIONAL_CARRIERHigh correlation
OPERATIONAL_CARRIER is highly correlated with MARKETING_CARRIERHigh correlation
STAY_LENGTH_D is highly correlated with SEGMENTSHigh correlation
FLIGHT_COUPONS is highly correlated with PAX_NHigh correlation
SEGMENTS is highly correlated with STAY_LENGTH_DHigh correlation
PAX_N is highly correlated with FLIGHT_COUPONSHigh correlation
UPGRADE_TYPE is highly correlated with BOOKING_DOMESTIC_FLAG and 1 other fieldsHigh correlation
BOOKING_DOMESTIC_FLAG is highly correlated with UPGRADE_TYPEHigh correlation
FLIGHT_RANGE is highly correlated with AIRCRAFT_TYPE and 1 other fieldsHigh correlation
BOOKING_ORIGIN_COUNTRY_CODE is highly correlated with BOOKING_LONG_HOUL_FLAGHigh correlation
AIRCRAFT_TYPE is highly correlated with FLIGHT_RANGE and 1 other fieldsHigh correlation
BOOKED_CLASS is highly correlated with BOOKED_CABINHigh correlation
CURRENCY is highly correlated with BOOKING_LONG_HOUL_FLAGHigh correlation
VAB is highly correlated with BOOKING_LONG_HOUL_FLAGHigh correlation
BOOKING_LONG_HOUL_FLAG is highly correlated with FLIGHT_RANGE and 5 other fieldsHigh correlation
UPGRADED_FLAG is highly correlated with UPGRADE_TYPEHigh correlation
BOOKED_CABIN is highly correlated with BOOKED_CLASSHigh correlation
BOOKING_DESTINATION_COUNTRY_CODE is highly correlated with BOOKING_LONG_HOUL_FLAGHigh correlation
FLIGHT_DISTANCE is highly correlated with FLIGHT_RANGE and 7 other fieldsHigh correlation
FLIGHT_RANGE is highly correlated with FLIGHT_DISTANCE and 5 other fieldsHigh correlation
MARKETING_CARRIER is highly correlated with OPERATIONAL_CARRIER and 1 other fieldsHigh correlation
OPERATIONAL_CARRIER is highly correlated with FLIGHT_DISTANCE and 3 other fieldsHigh correlation
BOOKED_CLASS is highly correlated with BOOKED_CABIN and 3 other fieldsHigh correlation
BOOKED_CABIN is highly correlated with BOOKED_CLASS and 3 other fieldsHigh correlation
AIRCRAFT_TYPE is highly correlated with FLIGHT_DISTANCE and 8 other fieldsHigh correlation
VAB is highly correlated with FLIGHT_DISTANCE and 7 other fieldsHigh correlation
UPGRADE_TYPE is highly correlated with BOOKED_CABINHigh correlation
ORIGINAL_TICKET_NUMBER is highly correlated with FORM_OF_PAYMENTHigh correlation
FORM_OF_PAYMENT is highly correlated with ORIGINAL_TICKET_NUMBER and 4 other fieldsHigh correlation
CURRENCY is highly correlated with FLIGHT_DISTANCE and 7 other fieldsHigh correlation
TOTAL_PRICE is highly correlated with CURRENCYHigh correlation
TOTAL_PRICE_PLN is highly correlated with BOOKED_CABIN and 1 other fieldsHigh correlation
PAX_TYPE is highly correlated with TOTAL_PRICE_PLNHigh correlation
SALES_MARKET is highly correlated with FORM_OF_PAYMENT and 4 other fieldsHigh correlation
SALES_CHANNEL is highly correlated with FORM_OF_PAYMENTHigh correlation
TRIP_TYPE is highly correlated with SEGMENTSHigh correlation
BOOKING_LONG_HOUL_FLAG is highly correlated with FLIGHT_DISTANCE and 7 other fieldsHigh correlation
FLIGHT_COUPONS is highly correlated with BOOKED_CLASS and 1 other fieldsHigh correlation
SEGMENTS is highly correlated with TRIP_TYPEHigh correlation
PAX_N is highly correlated with BOOKED_CLASS and 1 other fieldsHigh correlation
BOOKING_ORIGIN_COUNTRY_CODE is highly correlated with FLIGHT_DISTANCE and 8 other fieldsHigh correlation
BOOKING_DESTINATION_COUNTRY_CODE is highly correlated with FLIGHT_DISTANCE and 8 other fieldsHigh correlation
UPGRADE_TYPE has 9215782 (99.9%) missing values Missing
UPGRADE_SALES_DATE has 9215782 (99.9%) missing values Missing
LOYAL_CUSTOMER_ID has 7840881 (85.0%) missing values Missing
LOYAL_CUSTOMER_DATE_OF_BIRTH has 7848547 (85.1%) missing values Missing
LOYAL_CUSTOMER_REGISTERED_DATE has 7840881 (85.0%) missing values Missing
BOOKING_DESTINATION_AIRPORT has 1170692 (12.7%) missing values Missing
BOOKING_DESTINATION_COUNTRY_CODE has 1170692 (12.7%) missing values Missing
ORIGINAL_TICKET_NUMBER is highly skewed (γ1 = -50.30191515) Skewed
TOTAL_PRICE is highly skewed (γ1 = 117.8490263) Skewed
BOOKING_WINDOW_D has 166525 (1.8%) zeros Zeros
STAY_LENGTH_D has 321065 (3.5%) zeros Zeros

Reproduction

Analysis started2022-03-19 13:39:40.709376
Analysis finished2022-03-19 14:26:20.026961
Duration46 minutes and 39.32 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

TICKET_NUMBER
Real number (ℝ≥0)

Distinct3944503
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.000223243 × 1015
Minimum621301964
Maximum9.999996297 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.8 MiB
2022-03-19T15:26:20.262416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum621301964
5-th percentile4.994320244 × 1014
Q12.498187979 × 1015
median5.002537448 × 1015
Q37.498449973 × 1015
95-th percentile9.501769457 × 1015
Maximum9.999996297 × 1015
Range9.999995676 × 1015
Interquartile range (IQR)5.000261994 × 1015

Descriptive statistics

Standard deviation2.88698754 × 1015
Coefficient of variation (CV)0.5773717293
Kurtosis-1.199361223
Mean5.000223243 × 1015
Median Absolute Deviation (MAD)2.500166606 × 1015
Skewness0.0001303991867
Sum8.13413659 × 1018
Variance8.334697059 × 1030
MonotonicityNot monotonic
2022-03-19T15:26:20.416978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.232155151 × 10154
 
< 0.1%
8.660267309 × 10154
 
< 0.1%
1.63763202 × 10154
 
< 0.1%
1.659987109 × 10154
 
< 0.1%
1.406367008 × 10154
 
< 0.1%
4.580296817 × 10154
 
< 0.1%
9.40633882 × 10154
 
< 0.1%
3.331892901 × 10154
 
< 0.1%
2.349371658 × 10154
 
< 0.1%
1.01012609 × 10154
 
< 0.1%
Other values (3944493)9224547
> 99.9%
ValueCountFrequency (%)
6213019642
< 0.1%
17462379492
< 0.1%
65670712344
< 0.1%
67851071542
< 0.1%
76321368372
< 0.1%
96808102414
< 0.1%
1.047700429 × 10102
< 0.1%
1.073433151 × 10102
< 0.1%
1.769053404 × 10102
< 0.1%
1.839801168 × 10102
< 0.1%
ValueCountFrequency (%)
9.999996297 × 10152
< 0.1%
9.999994496 × 10154
< 0.1%
9.999993161 × 10154
< 0.1%
9.999991198 × 10151
 
< 0.1%
9.999988824 × 10152
< 0.1%
9.999980413 × 10154
< 0.1%
9.999980094 × 10152
< 0.1%
9.99997584 × 10152
< 0.1%
9.999972103 × 10152
< 0.1%
9.999958445 × 10154
< 0.1%

COUPON_NUMBER
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
1
3944455 
2
3192507 
3
1048568 
4
1039057 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
13944455
42.8%
23192507
34.6%
31048568
 
11.4%
41039057
 
11.3%

Length

2022-03-19T15:26:20.563937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-19T15:26:20.656532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
13944455
42.8%
23192507
34.6%
31048568
 
11.4%
41039057
 
11.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ORIGIN_AIRPORT_CODE
Categorical

HIGH CARDINALITY

Distinct657
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
OUO
4077079 
LFF
 
346275
EIE
 
182903
LFN
 
146284
VMX
 
143546
Other values (652)
4328500 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)< 0.1%

Sample

1st rowWXA
2nd rowOUO
3rd rowOUO
4th rowDZN
5th rowLFF

Common Values

ValueCountFrequency (%)
OUO4077079
44.2%
LFF346275
 
3.8%
EIE182903
 
2.0%
LFN146284
 
1.6%
VMX143546
 
1.6%
GMW140460
 
1.5%
UGK140279
 
1.5%
FBI136799
 
1.5%
WXA128991
 
1.4%
NTH128869
 
1.4%
Other values (647)3653102
39.6%

Length

2022-03-19T15:26:20.768541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ouo4077079
44.2%
lff346275
 
3.8%
eie182903
 
2.0%
lfn146284
 
1.6%
vmx143546
 
1.6%
gmw140460
 
1.5%
ugk140279
 
1.5%
fbi136799
 
1.5%
wxa128991
 
1.4%
nth128869
 
1.4%
Other values (647)3653102
39.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DESTINATION_AIRPORT_CODE
Categorical

HIGH CARDINALITY

Distinct679
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
OUO
4121121 
LFF
 
325506
EIE
 
172335
LFN
 
149960
FBI
 
145271
Other values (674)
4310394 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)< 0.1%

Sample

1st rowOUO
2nd rowRRS
3rd rowHIX
4th rowOUO
5th rowNTH

Common Values

ValueCountFrequency (%)
OUO4121121
44.7%
LFF325506
 
3.5%
EIE172335
 
1.9%
LFN149960
 
1.6%
FBI145271
 
1.6%
UGK144139
 
1.6%
GMW141766
 
1.5%
VMX140077
 
1.5%
HIX129836
 
1.4%
UIT129380
 
1.4%
Other values (669)3625196
39.3%

Length

2022-03-19T15:26:20.879464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ouo4121121
44.7%
lff325506
 
3.5%
eie172335
 
1.9%
lfn149960
 
1.6%
fbi145271
 
1.6%
ugk144139
 
1.6%
gmw141766
 
1.5%
vmx140077
 
1.5%
hix129836
 
1.4%
uit129380
 
1.4%
Other values (669)3625196
39.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FLIGHT_DATE_LOCAL
Categorical

HIGH CARDINALITY

Distinct727
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
2007-09-21
 
34059
2007-09-14
 
33767
2007-09-17
 
33341
2007-09-28
 
32822
2007-09-24
 
32600
Other values (722)
9057998 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row2007-05-16
2nd row2007-08-26
3rd row2007-03-20
4th row2007-04-08
5th row2007-01-17

Common Values

ValueCountFrequency (%)
2007-09-2134059
 
0.4%
2007-09-1433767
 
0.4%
2007-09-1733341
 
0.4%
2007-09-2832822
 
0.4%
2007-09-2432600
 
0.4%
2007-08-1032328
 
0.4%
2007-07-0932304
 
0.4%
2007-09-1032280
 
0.3%
2007-09-0732212
 
0.3%
2007-07-2032204
 
0.3%
Other values (717)8896670
96.4%

Length

2022-03-19T15:26:20.980234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2007-09-2134059
 
0.4%
2007-09-1433767
 
0.4%
2007-09-1733341
 
0.4%
2007-09-2832822
 
0.4%
2007-09-2432600
 
0.4%
2007-08-1032328
 
0.4%
2007-07-0932304
 
0.4%
2007-09-1032280
 
0.3%
2007-09-0732212
 
0.3%
2007-07-2032204
 
0.3%
Other values (717)8896670
96.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TIME_DEPARTURE_LOCAL_TIME
Categorical

HIGH CARDINALITY

Distinct1051
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size140.8 MiB
00:00:00
 
226887
19:50:00
 
200875
14:40:00
 
171883
16:45:00
 
164385
07:20:00
 
164203
Other values (1046)
8296352 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)< 0.1%

Sample

1st row19:40:00
2nd row13:55:00
3rd row16:40:00
4th row06:05:00
5th row18:05:00

Common Values

ValueCountFrequency (%)
00:00:00226887
 
2.5%
19:50:00200875
 
2.2%
14:40:00171883
 
1.9%
16:45:00164385
 
1.8%
07:20:00164203
 
1.8%
14:55:00150588
 
1.6%
17:05:00146615
 
1.6%
19:45:00133143
 
1.4%
16:30:00128424
 
1.4%
19:40:00127238
 
1.4%
Other values (1041)7610344
82.5%

Length

2022-03-19T15:26:21.084280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00226887
 
2.5%
19:50:00200875
 
2.2%
14:40:00171883
 
1.9%
16:45:00164385
 
1.8%
07:20:00164203
 
1.8%
14:55:00150588
 
1.6%
17:05:00146615
 
1.6%
19:45:00133143
 
1.4%
16:30:00128424
 
1.4%
19:40:00127238
 
1.4%
Other values (1041)7610344
82.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FLIGHT_DISTANCE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1688
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1814.180651
Minimum0
Maximum14507
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size140.8 MiB
2022-03-19T15:26:22.026443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile252
Q1550
median984
Q31460
95-th percentile7521
Maximum14507
Range14507
Interquartile range (IQR)910

Descriptive statistics

Standard deviation2303.818296
Coefficient of variation (CV)1.269894646
Kurtosis3.009986926
Mean1814.180651
Median Absolute Deviation (MAD)447
Skewness2.093692771
Sum1.673506725 × 1010
Variance5307578.742
MonotonicityNot monotonic
2022-03-19T15:26:22.140896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
246405676
 
4.4%
1460282015
 
3.1%
1344255041
 
2.8%
1102245560
 
2.7%
984229948
 
2.5%
721217810
 
2.4%
2511217445
 
2.4%
402214271
 
2.3%
1160200195
 
2.2%
7521194260
 
2.1%
Other values (1678)6762366
73.3%
ValueCountFrequency (%)
08
 
< 0.1%
92
 
< 0.1%
628
 
< 0.1%
90135
< 0.1%
1031
 
< 0.1%
1081
 
< 0.1%
1104
 
< 0.1%
1172
 
< 0.1%
1192
 
< 0.1%
1289
 
< 0.1%
ValueCountFrequency (%)
145071
 
< 0.1%
141314
 
< 0.1%
131751
 
< 0.1%
124661
 
< 0.1%
122406
 
< 0.1%
121782
 
< 0.1%
120051
 
< 0.1%
114971
 
< 0.1%
108902
 
< 0.1%
108691105
< 0.1%

FLIGHT_RANGE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size140.8 MiB
SHORT-HAUL
7061558 
LONG-HAUL
1166541 
DOMESTIC
996319 
UNKNOWN
 
167

Length

Max length10
Median length10
Mean length9.657471854
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSHORT-HAUL
2nd rowLONG-HAUL
3rd rowLONG-HAUL
4th rowSHORT-HAUL
5th rowSHORT-HAUL

Common Values

ValueCountFrequency (%)
SHORT-HAUL7061558
76.6%
LONG-HAUL1166541
 
12.6%
DOMESTIC996319
 
10.8%
UNKNOWN167
 
< 0.1%
(Missing)2
 
< 0.1%

Length

2022-03-19T15:26:22.256778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-19T15:26:22.324876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
short-haul7061558
76.6%
long-haul1166541
 
12.6%
domestic996319
 
10.8%
unknown167
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MARKETING_CARRIER
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct89
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.472350871 × 1015
Minimum2.268751528 × 1014
Maximum9.838732082 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.8 MiB
2022-03-19T15:26:22.413435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.268751528 × 1014
5-th percentile2.434615205 × 1015
Q12.434615205 × 1015
median2.434615205 × 1015
Q32.434615205 × 1015
95-th percentile2.434615205 × 1015
Maximum9.838732082 × 1015
Range9.611856929 × 1015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.36700163 × 1014
Coefficient of variation (CV)0.176633571
Kurtosis131.2053692
Mean2.472350871 × 1015
Median Absolute Deviation (MAD)0
Skewness10.95075707
Sum6.240027701 × 1018
Variance1.907070324 × 1029
MonotonicityNot monotonic
2022-03-19T15:26:22.533076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.434615205 × 10159077702
98.4%
5.403774244 × 101525368
 
0.3%
2.099453761 × 101519567
 
0.2%
1.319526191 × 101511766
 
0.1%
7.495563552 × 101511634
 
0.1%
7.711540388 × 10157700
 
0.1%
4.849361541 × 10156952
 
0.1%
7.346940931 × 10156690
 
0.1%
3.858052475 × 10155809
 
0.1%
8.304362752 × 10155434
 
0.1%
Other values (79)45965
 
0.5%
ValueCountFrequency (%)
2.268751528 × 101410
 
< 0.1%
2.455475406 × 10142923
< 0.1%
3.557699075 × 1014214
 
< 0.1%
6.314863066 × 1014342
 
< 0.1%
8.264698432 × 101416
 
< 0.1%
8.925619808 × 1014284
 
< 0.1%
1.040662998 × 101559
 
< 0.1%
1.062886389 × 101520
 
< 0.1%
1.181685408 × 10159
 
< 0.1%
1.268037904 × 10151242
< 0.1%
ValueCountFrequency (%)
9.838732082 × 10154
 
< 0.1%
9.76316395 × 10151529
< 0.1%
9.590400398 × 1015127
 
< 0.1%
9.411423626 × 10151255
< 0.1%
9.379265973 × 10152
 
< 0.1%
9.265787999 × 1015708
< 0.1%
9.228032722 × 101583
 
< 0.1%
8.804634256 × 1015661
< 0.1%
8.642649904 × 1015948
< 0.1%
8.511667559 × 1015596
 
< 0.1%

OPERATIONAL_CARRIER
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct90
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.552896636 × 1015
Minimum2.268751528 × 1014
Maximum9.838732082 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.8 MiB
2022-03-19T15:26:22.653789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.268751528 × 1014
5-th percentile2.434615205 × 1015
Q12.434615205 × 1015
median2.434615205 × 1015
Q32.434615205 × 1015
95-th percentile2.434615205 × 1015
Maximum9.838732082 × 1015
Range9.611856929 × 1015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.563456611 × 1014
Coefficient of variation (CV)0.2962695985
Kurtosis42.09296726
Mean2.552896636 × 1015
Median Absolute Deviation (MAD)0
Skewness6.350476514
Sum-7.075065803 × 1018
Variance5.720587591 × 1029
MonotonicityNot monotonic
2022-03-19T15:26:22.780071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.434615205 × 10158870764
96.2%
5.403774244 × 101570623
 
0.8%
7.495563552 × 101542591
 
0.5%
4.676356572 × 101535092
 
0.4%
8.804634256 × 101531458
 
0.3%
2.099453761 × 101522353
 
0.2%
7.346940931 × 101519166
 
0.2%
6.990908407 × 101518889
 
0.2%
1.319526191 × 101514307
 
0.2%
1.711731043 × 101512613
 
0.1%
Other values (80)86731
 
0.9%
ValueCountFrequency (%)
2.268751528 × 101410
 
< 0.1%
2.455475406 × 10145001
0.1%
3.557699075 × 1014214
 
< 0.1%
6.314863066 × 1014342
 
< 0.1%
8.264698432 × 101416
 
< 0.1%
8.925619808 × 1014284
 
< 0.1%
1.040662998 × 101559
 
< 0.1%
1.062886389 × 101520
 
< 0.1%
1.181685408 × 10159
 
< 0.1%
1.268037904 × 10155228
0.1%
ValueCountFrequency (%)
9.838732082 × 10154
 
< 0.1%
9.76316395 × 10151529
 
< 0.1%
9.590400398 × 1015127
 
< 0.1%
9.411423626 × 10151255
 
< 0.1%
9.379265973 × 10152
 
< 0.1%
9.265787999 × 1015707
 
< 0.1%
9.228032722 × 101583
 
< 0.1%
8.804634256 × 101531458
0.3%
8.642649904 × 1015948
 
< 0.1%
8.511667559 × 1015596
 
< 0.1%

BOOKED_CLASS
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
L
1208736 
W
1159102 
V
1008351 
U
1000495 
S
769101 
Other values (21)
4078802 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowO
2nd rowU
3rd rowU
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
L1208736
13.1%
W1159102
12.6%
V1008351
10.9%
U1000495
10.8%
S769101
8.3%
T707038
7.7%
O703695
7.6%
Q555528
6.0%
G415344
 
4.5%
K372493
 
4.0%
Other values (16)1324704
14.4%

Length

2022-03-19T15:26:22.889211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
l1208736
13.1%
w1159102
12.6%
v1008351
10.9%
u1000495
10.8%
s769101
8.3%
t707038
7.7%
o703695
7.6%
q555528
6.0%
g415344
 
4.5%
k372493
 
4.0%
Other values (15)1324703
14.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BOOKED_CABIN
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size140.8 MiB
Economy
8708423 
Premium
 
280443
Business
 
235720

Length

Max length8
Median length7
Mean length7.02555345
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEconomy
2nd rowEconomy
3rd rowEconomy
4th rowEconomy
5th rowEconomy

Common Values

ValueCountFrequency (%)
Economy8708423
94.4%
Premium280443
 
3.0%
Business235720
 
2.6%
(Missing)1
 
< 0.1%

Length

2022-03-19T15:26:22.987445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-19T15:26:23.050402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
economy8708423
94.4%
premium280443
 
3.0%
business235720
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AIRCRAFT_TYPE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
DH4
1123835 
E75
1091080 
E95
895297 
738
776429 
788
715728 
Other values (60)
4622218 

Length

Max length7
Median length3
Mean length3.073440253
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row738
2nd row788
3rd row788
4th rowE75
5th rowCRN

Common Values

ValueCountFrequency (%)
DH41123835
12.2%
E751091080
11.8%
E95895297
9.7%
738776429
8.4%
788715728
 
7.8%
CR9691160
 
7.5%
7M8640839
 
6.9%
E9A557339
 
6.0%
E70477442
 
5.2%
73H435696
 
4.7%
Other values (55)1819742
19.7%

Length

2022-03-19T15:26:23.127685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dh41123835
12.2%
e751091080
11.8%
e95895297
9.7%
738776429
8.4%
788715728
 
7.8%
cr9691160
 
7.5%
7m8640839
 
6.9%
e9a557339
 
6.0%
e70477442
 
5.2%
73h435696
 
4.7%
Other values (55)1819742
19.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FARE_BASIS
Categorical

HIGH CARDINALITY

Distinct33211
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
GIT
 
222686
LSAVI0
 
95304
USAVI0
 
87918
OZSAVJ0
 
70761
VSAVK0
 
69933
Other values (33206)
8677985 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3759 ?
Unique (%)< 0.1%

Sample

1st rowOZSTDJ0
2nd rowUKPRO10
3rd rowULPRO11
4th rowWSTDK0
5th rowWCSERT/UD10

Common Values

ValueCountFrequency (%)
GIT 222686
 
2.4%
LSAVI0 95304
 
1.0%
USAVI0 87918
 
1.0%
OZSAVJ0 70761
 
0.8%
VSAVK0 69933
 
0.8%
WSAVK0 69253
 
0.8%
WSAVI0 67337
 
0.7%
TLOSEA 65120
 
0.7%
VSTDK0 56695
 
0.6%
USAVG0 55945
 
0.6%
Other values (33201)8363635
90.7%

Length

2022-03-19T15:26:23.233482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
git222696
 
2.4%
lsavi095309
 
1.0%
usavi087929
 
1.0%
ozsavj070778
 
0.8%
vsavk069936
 
0.8%
wsavk069258
 
0.8%
wsavi067349
 
0.7%
tlosea65120
 
0.7%
vstdk056703
 
0.6%
usavg055970
 
0.6%
Other values (32329)8368540
90.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VAB
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
SAVER
3251123 
STANDARD
2563924 
OTHER
2098035 
FLEX
699229 
SEMI-FLEX
348690 
Other values (2)
 
263586

Length

Max length9
Median length5
Mean length5.938575028
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTANDARD
2nd rowBASIC
3rd rowBASIC
4th rowSTANDARD
5th rowOTHER

Common Values

ValueCountFrequency (%)
SAVER3251123
35.2%
STANDARD2563924
27.8%
OTHER2098035
22.7%
FLEX699229
 
7.6%
SEMI-FLEX348690
 
3.8%
BASIC195920
 
2.1%
FULL-FLEX67666
 
0.7%

Length

2022-03-19T15:26:23.328424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-19T15:26:23.397698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
saver3251123
35.2%
standard2563924
27.8%
other2098035
22.7%
flex699229
 
7.6%
semi-flex348690
 
3.8%
basic195920
 
2.1%
full-flex67666
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UPGRADED_FLAG
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 MiB
False
9215782 
True
 
8805
ValueCountFrequency (%)
False9215782
99.9%
True8805
 
0.1%
2022-03-19T15:26:23.462526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

UPGRADE_TYPE
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.1%
Missing9215782
Missing (%)99.9%
Memory size140.8 MiB
lot upgrade
3397 
upgrade
1966 
upgrade at gate
1359 
upg econ to premium
1046 
upg econ to business
602 

Length

Max length23
Median length11
Mean length12.88279387
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowupg premium to business
2nd rowlot upgrade
3rd rowupgrade
4th rowlot upgrade
5th rowlot upgrade

Common Values

ValueCountFrequency (%)
lot upgrade3397
 
< 0.1%
upgrade1966
 
< 0.1%
upgrade at gate1359
 
< 0.1%
upg econ to premium1046
 
< 0.1%
upg econ to business602
 
< 0.1%
upg premium to business435
 
< 0.1%
(Missing)9215782
99.9%

Length

2022-03-19T15:26:23.530797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-19T15:26:23.606604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
upgrade6722
31.8%
lot3397
16.0%
upg2083
 
9.8%
to2083
 
9.8%
econ1648
 
7.8%
premium1481
 
7.0%
at1359
 
6.4%
gate1359
 
6.4%
business1037
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UPGRADE_SALES_DATE
Categorical

HIGH CARDINALITY
MISSING

Distinct481
Distinct (%)5.5%
Missing9215782
Missing (%)99.9%
Memory size140.8 MiB
2007-11-05
 
52
2007-10-15
 
52
2007-12-15
 
50
2007-09-25
 
49
2007-09-29
 
48
Other values (476)
8554 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)0.7%

Sample

1st row2007-01-11
2nd row2007-02-04
3rd row2007-05-15
4th row2007-01-24
5th row2007-02-04

Common Values

ValueCountFrequency (%)
2007-11-0552
 
< 0.1%
2007-10-1552
 
< 0.1%
2007-12-1550
 
< 0.1%
2007-09-2549
 
< 0.1%
2007-09-2948
 
< 0.1%
2007-08-2147
 
< 0.1%
2007-05-2447
 
< 0.1%
2007-09-0447
 
< 0.1%
2007-05-2747
 
< 0.1%
2007-12-2647
 
< 0.1%
Other values (471)8319
 
0.1%
(Missing)9215782
99.9%

Length

2022-03-19T15:26:23.692174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2007-11-0552
 
0.6%
2007-10-1552
 
0.6%
2007-12-1550
 
0.6%
2007-09-2549
 
0.6%
2007-09-2948
 
0.5%
2007-08-2147
 
0.5%
2007-05-2447
 
0.5%
2007-09-0447
 
0.5%
2007-05-2747
 
0.5%
2007-12-2647
 
0.5%
Other values (471)8319
94.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BOOKING_ID
Real number (ℝ≥0)

Distinct2795940
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.999167484 × 1015
Minimum1.4787909 × 1010
Maximum9.999993604 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.8 MiB
2022-03-19T15:26:23.814111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.4787909 × 1010
5-th percentile4.996979081 × 1014
Q12.502587582 × 1015
median4.995274085 × 1015
Q37.500628424 × 1015
95-th percentile9.498853657 × 1015
Maximum9.999993604 × 1015
Range9.999978816 × 1015
Interquartile range (IQR)4.998040842 × 1015

Descriptive statistics

Standard deviation2.885806653 × 1015
Coefficient of variation (CV)0.5772574459
Kurtosis-1.199414561
Mean4.999167484 × 1015
Median Absolute Deviation (MAD)2.498697906 × 1015
Skewness0.00160428563
Sum-1.604802714 × 1018
Variance8.327880039 × 1030
MonotonicityNot monotonic
2022-03-19T15:26:23.946501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.814006325 × 1015279
 
< 0.1%
6.413885912 × 1015248
 
< 0.1%
9.330436931 × 1015232
 
< 0.1%
8.124407744 × 1015222
 
< 0.1%
9.20838413 × 1015220
 
< 0.1%
4.073011969 × 1014216
 
< 0.1%
5.503728085 × 1015216
 
< 0.1%
9.845896655 × 1015216
 
< 0.1%
2.741427892 × 1015212
 
< 0.1%
5.469900034 × 1015210
 
< 0.1%
Other values (2795930)9222316
> 99.9%
ValueCountFrequency (%)
1.4787909 × 10102
 
< 0.1%
1.708659761 × 10104
 
< 0.1%
1.996479238 × 10102
 
< 0.1%
2.421156679 × 10102
 
< 0.1%
3.152591026 × 10104
 
< 0.1%
3.300183668 × 10102
 
< 0.1%
4.150214043 × 10104
 
< 0.1%
4.183495346 × 10104
 
< 0.1%
4.396179757 × 10108
< 0.1%
4.641041529 × 101016
< 0.1%
ValueCountFrequency (%)
9.999993604 × 10152
 
< 0.1%
9.999991575 × 10152
 
< 0.1%
9.999988806 × 10154
< 0.1%
9.99998833 × 10154
< 0.1%
9.999985817 × 10158
< 0.1%
9.999985519 × 10152
 
< 0.1%
9.999982392 × 10152
 
< 0.1%
9.99998141 × 10152
 
< 0.1%
9.999970594 × 10152
 
< 0.1%
9.999969169 × 10152
 
< 0.1%

ORIGINAL_TICKET_NUMBER
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct3310
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.376164102 × 1015
Minimum2.334519661 × 1012
Maximum9.998792831 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.8 MiB
2022-03-19T15:26:24.081405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.334519661 × 1012
5-th percentile9.379265973 × 1015
Q19.379265973 × 1015
median9.379265973 × 1015
Q39.379265973 × 1015
95-th percentile9.379265973 × 1015
Maximum9.998792831 × 1015
Range9.996458311 × 1015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.395686988 × 1014
Coefficient of variation (CV)0.01488547953
Kurtosis2696.179345
Mean9.376164102 × 1015
Median Absolute Deviation (MAD)0
Skewness-50.30191515
Sum-5.541480552 × 1018
Variance1.94794217 × 1028
MonotonicityNot monotonic
2022-03-19T15:26:24.206196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.379265973 × 10159218034
99.9%
3.981966873 × 101510
 
< 0.1%
7.971162458 × 101310
 
< 0.1%
1.306124545 × 101510
 
< 0.1%
8.460182694 × 101510
 
< 0.1%
4.834231548 × 10158
 
< 0.1%
7.62507682 × 10158
 
< 0.1%
9.568725185 × 10158
 
< 0.1%
1.721215489 × 10157
 
< 0.1%
6.82357158 × 10156
 
< 0.1%
Other values (3300)6476
 
0.1%
ValueCountFrequency (%)
2.334519661 × 10122
< 0.1%
8.145144489 × 10122
< 0.1%
1.002984636 × 10131
 
< 0.1%
1.368096309 × 10131
 
< 0.1%
2.110182871 × 10132
< 0.1%
2.173339016 × 10134
< 0.1%
4.265450911 × 10134
< 0.1%
4.373848398 × 10131
 
< 0.1%
4.55339565 × 10132
< 0.1%
4.70488326 × 10132
< 0.1%
ValueCountFrequency (%)
9.998792831 × 10151
< 0.1%
9.99759638 × 10152
< 0.1%
9.995602871 × 10152
< 0.1%
9.995032605 × 10152
< 0.1%
9.993920037 × 10152
< 0.1%
9.987224125 × 10151
< 0.1%
9.976617261 × 10151
< 0.1%
9.975232462 × 10152
< 0.1%
9.962332105 × 10151
< 0.1%
9.961439234 × 10151
< 0.1%

FORM_OF_PAYMENT
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct79
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
CA
3181526 
CCCA5
2552070 
CCVI4
1706369 
CCAX3
720072 
CCTP1
 
270349
Other values (74)
794201 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowCA
3rd rowCCVI4
4th rowCA
5th rowCA

Common Values

ValueCountFrequency (%)
CA 3181526
34.5%
CCCA52552070
27.7%
CCVI41706369
18.5%
CCAX3720072
 
7.8%
CCTP1270349
 
2.9%
DOTPA235214
 
2.5%
PAYPA168891
 
1.8%
CCDC3120727
 
1.3%
INVOI103639
 
1.1%
INTER38002
 
0.4%
Other values (69)127728
 
1.4%

Length

2022-03-19T15:26:24.329732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca3181526
34.5%
ccca52552070
27.7%
ccvi41706369
18.5%
ccax3720072
 
7.8%
cctp1270349
 
2.9%
dotpa235214
 
2.5%
paypa168891
 
1.8%
ccdc3120727
 
1.3%
invoi103639
 
1.1%
inter38002
 
0.4%
Other values (70)131488
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CURRENCY
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
EUR
2981011 
PLN
2323316 
USD
1142780 
SEK
597344 
UAH
315625 
Other values (33)
1864511 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPLN
2nd rowEUR
3rd rowUSD
4th rowUAH
5th rowEUR

Common Values

ValueCountFrequency (%)
EUR2981011
32.3%
PLN2323316
25.2%
USD1142780
 
12.4%
SEK597344
 
6.5%
UAH315625
 
3.4%
RUB273962
 
3.0%
GBP271065
 
2.9%
CAD234220
 
2.5%
KRW155622
 
1.7%
DKK122084
 
1.3%
Other values (28)807558
 
8.8%

Length

2022-03-19T15:26:24.432741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
eur2981011
32.3%
pln2323316
25.2%
usd1142780
 
12.4%
sek597344
 
6.5%
uah315625
 
3.4%
rub273962
 
3.0%
gbp271065
 
2.9%
cad234220
 
2.5%
krw155622
 
1.7%
dkk122084
 
1.3%
Other values (27)807529
 
8.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TOTAL_PRICE
Real number (ℝ)

HIGH CORRELATION
SKEWED

Distinct363032
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25080.2803
Minimum-1564.6
Maximum107910000
Zeros4226
Zeros (%)< 0.1%
Negative3
Negative (%)< 0.1%
Memory size140.8 MiB
2022-03-19T15:26:24.570494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1564.6
5-th percentile130.99
Q1280.11
median786.14
Q31819
95-th percentile23907
Maximum107910000
Range107911564.6
Interquartile range (IQR)1538.89

Descriptive statistics

Standard deviation223500.6036
Coefficient of variation (CV)8.911407724
Kurtosis38421.76424
Mean25080.2803
Median Absolute Deviation (MAD)560.67
Skewness117.8490263
Sum2.313552276 × 1011
Variance4.995251981 × 1010
MonotonicityNot monotonic
2022-03-19T15:26:24.707234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04226
 
< 0.1%
2402720
 
< 0.1%
229.341994
 
< 0.1%
99.91866
 
< 0.1%
189.521695
 
< 0.1%
209.341679
 
< 0.1%
9801598
 
< 0.1%
156.211532
 
< 0.1%
25321395
 
< 0.1%
190.61384
 
< 0.1%
Other values (363022)9204498
99.8%
ValueCountFrequency (%)
-1564.63
 
< 0.1%
04226
< 0.1%
0.032
 
< 0.1%
0.042
 
< 0.1%
0.082
 
< 0.1%
0.12
 
< 0.1%
0.24
 
< 0.1%
0.5328
 
< 0.1%
0.61
 
< 0.1%
162
 
< 0.1%
ValueCountFrequency (%)
1079100004
< 0.1%
715120002
 
< 0.1%
645370003
 
< 0.1%
594520002
 
< 0.1%
589390004
< 0.1%
578720002
 
< 0.1%
554120006
< 0.1%
507790008
< 0.1%
378740002
 
< 0.1%
355010002
 
< 0.1%

TOTAL_PRICE_PLN
Real number (ℝ)

HIGH CORRELATION

Distinct473960
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1688.65867
Minimum-5328.25
Maximum49075.99
Zeros4226
Zeros (%)< 0.1%
Negative3
Negative (%)< 0.1%
Memory size140.8 MiB
2022-03-19T15:26:24.838835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-5328.25
5-th percentile432.78
Q1782.51
median1162.92
Q32052
95-th percentile4336.297
Maximum49075.99
Range54404.24
Interquartile range (IQR)1269.49

Descriptive statistics

Standard deviation1672.056032
Coefficient of variation (CV)0.9901681501
Kurtosis34.25929433
Mean1688.65867
Median Absolute Deviation (MAD)481.36
Skewness4.427305766
Sum1.557717882 × 1010
Variance2795771.373
MonotonicityNot monotonic
2022-03-19T15:26:24.962114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04226
 
< 0.1%
60.591150
 
< 0.1%
1371.08965
 
< 0.1%
1239.49828
 
< 0.1%
429.79563
 
< 0.1%
399.43400
 
< 0.1%
1429.81381
 
< 0.1%
349.07375
 
< 0.1%
3211.84339
 
< 0.1%
1759.05318
 
< 0.1%
Other values (473950)9215042
99.9%
ValueCountFrequency (%)
-5328.253
 
< 0.1%
04226
< 0.1%
0.032
 
< 0.1%
0.082
 
< 0.1%
0.172
 
< 0.1%
0.23
 
< 0.1%
0.432
 
< 0.1%
0.671
 
< 0.1%
0.851
 
< 0.1%
0.94
 
< 0.1%
ValueCountFrequency (%)
49075.994
< 0.1%
48898.134
< 0.1%
42351.544
< 0.1%
40716.984
< 0.1%
40447.813
< 0.1%
40301.444
< 0.1%
37710.933
< 0.1%
35749.986
< 0.1%
35723.834
< 0.1%
35681.944
< 0.1%

PAX_GENDER
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
M
6549164 
F
2675423 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M6549164
71.0%
F2675423
29.0%

Length

2022-03-19T15:26:25.078157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-19T15:26:25.139571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
m6549164
71.0%
f2675423
29.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PAX_TYPE
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
ADULT
9045461 
CHILD
 
144947
INFANT
 
34179

Length

Max length6
Median length5
Mean length5.003705207
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADULT
2nd rowADULT
3rd rowADULT
4th rowADULT
5th rowADULT

Common Values

ValueCountFrequency (%)
ADULT9045461
98.1%
CHILD144947
 
1.6%
INFANT34179
 
0.4%

Length

2022-03-19T15:26:25.198852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-19T15:26:25.263714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
adult9045461
98.1%
child144947
 
1.6%
infant34179
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 MiB
False
8952085 
True
 
272502
ValueCountFrequency (%)
False8952085
97.0%
True272502
 
3.0%
2022-03-19T15:26:25.306260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 MiB
False
7840881 
True
1383706 
ValueCountFrequency (%)
False7840881
85.0%
True1383706
 
15.0%
2022-03-19T15:26:25.355678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

LOYAL_CUSTOMER_ID
Real number (ℝ≥0)

MISSING

Distinct265358
Distinct (%)19.2%
Missing7840881
Missing (%)85.0%
Infinite0
Infinite (%)0.0%
Mean4.984529267 × 1015
Minimum1.389848426 × 1010
Maximum9.999998028 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.8 MiB
2022-03-19T15:26:25.445516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.389848426 × 1010
5-th percentile4.887175725 × 1014
Q12.483612606 × 1015
median4.977264159 × 1015
Q37.484234464 × 1015
95-th percentile9.490646602 × 1015
Maximum9.999998028 × 1015
Range9.999984129 × 1015
Interquartile range (IQR)5.000621858 × 1015

Descriptive statistics

Standard deviation2.889320868 × 1015
Coefficient of variation (CV)0.5796577196
Kurtosis-1.198005069
Mean4.984529267 × 1015
Median Absolute Deviation (MAD)2.501096772 × 1015
Skewness0.005668665518
Sum6.897123054 × 1021
Variance8.34817508 × 1030
MonotonicityNot monotonic
2022-03-19T15:26:25.591208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.910455974 × 1015216
 
< 0.1%
7.649390971 × 1015171
 
< 0.1%
6.057484602 × 1015160
 
< 0.1%
8.30918744 × 1014158
 
< 0.1%
5.789314198 × 1014155
 
< 0.1%
9.922290221 × 1015150
 
< 0.1%
7.052728341 × 1015148
 
< 0.1%
8.957362722 × 1015144
 
< 0.1%
4.953263798 × 1015144
 
< 0.1%
3.020371903 × 1015141
 
< 0.1%
Other values (265348)1382119
 
15.0%
(Missing)7840881
85.0%
ValueCountFrequency (%)
1.389848426 × 10102
 
< 0.1%
1.695067404 × 10114
 
< 0.1%
1.790219526 × 101121
< 0.1%
2.267783645 × 10116
 
< 0.1%
2.567251252 × 10114
 
< 0.1%
2.608901449 × 10114
 
< 0.1%
3.200981632 × 10111
 
< 0.1%
3.645997832 × 10114
 
< 0.1%
3.795908228 × 10111
 
< 0.1%
4.320790139 × 101114
< 0.1%
ValueCountFrequency (%)
9.999998028 × 10152
 
< 0.1%
9.999983475 × 10151
 
< 0.1%
9.999910337 × 10154
< 0.1%
9.999840581 × 10154
< 0.1%
9.999808045 × 10152
 
< 0.1%
9.999776868 × 10152
 
< 0.1%
9.999767465 × 10159
< 0.1%
9.999753454 × 10154
< 0.1%
9.999691793 × 10158
< 0.1%
9.999579353 × 10156
< 0.1%

LOYAL_CUSTOMER_DATE_OF_BIRTH
Categorical

HIGH CARDINALITY
MISSING

Distinct24920
Distinct (%)1.8%
Missing7848547
Missing (%)85.1%
Memory size140.8 MiB
1964-04-20
 
358
1967-08-25
 
358
1969-07-18
 
332
1971-05-21
 
326
1960-11-23
 
309
Other values (24915)
1374357 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique222 ?
Unique (%)< 0.1%

Sample

1st row1961-03-26
2nd row1979-09-12
3rd row1951-10-15
4th row1943-11-01
5th row1966-07-04

Common Values

ValueCountFrequency (%)
1964-04-20358
 
< 0.1%
1967-08-25358
 
< 0.1%
1969-07-18332
 
< 0.1%
1971-05-21326
 
< 0.1%
1960-11-23309
 
< 0.1%
1967-03-03305
 
< 0.1%
1966-07-21304
 
< 0.1%
1970-12-20304
 
< 0.1%
1970-06-12302
 
< 0.1%
1967-04-14302
 
< 0.1%
Other values (24910)1372840
 
14.9%
(Missing)7848547
85.1%

Length

2022-03-19T15:26:25.975343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1964-04-20358
 
< 0.1%
1967-08-25358
 
< 0.1%
1969-07-18332
 
< 0.1%
1971-05-21326
 
< 0.1%
1960-11-23309
 
< 0.1%
1967-03-03305
 
< 0.1%
1966-07-21304
 
< 0.1%
1970-12-20304
 
< 0.1%
1970-06-12302
 
< 0.1%
1967-04-14302
 
< 0.1%
Other values (24910)1372840
99.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LOYAL_CUSTOMER_REGISTERED_DATE
Categorical

HIGH CARDINALITY
MISSING

Distinct8930
Distinct (%)0.6%
Missing7840881
Missing (%)85.0%
Memory size140.8 MiB
1991-12-01
 
19093
1991-11-30
 
10357
1991-12-03
 
7600
1991-11-29
 
5689
1996-06-14
 
4776
Other values (8925)
1336191 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)< 0.1%

Sample

1st row1996-06-13
2nd row2006-10-22
3rd row1986-04-03
4th row1988-05-27
5th row2003-11-21

Common Values

ValueCountFrequency (%)
1991-12-0119093
 
0.2%
1991-11-3010357
 
0.1%
1991-12-037600
 
0.1%
1991-11-295689
 
0.1%
1996-06-144776
 
0.1%
1996-06-134724
 
0.1%
1992-02-184149
 
< 0.1%
1996-08-204147
 
< 0.1%
1992-02-203318
 
< 0.1%
1996-06-242828
 
< 0.1%
Other values (8920)1317025
 
14.3%
(Missing)7840881
85.0%

Length

2022-03-19T15:26:26.081222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1991-12-0119093
 
1.4%
1991-11-3010357
 
0.7%
1991-12-037600
 
0.5%
1991-11-295689
 
0.4%
1996-06-144776
 
0.3%
1996-06-134724
 
0.3%
1992-02-184149
 
0.3%
1996-08-204147
 
0.3%
1992-02-203318
 
0.2%
1996-06-242828
 
0.2%
Other values (8920)1317025
95.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SALES_DATE
Categorical

HIGH CARDINALITY

Distinct365
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
2007-01-10
 
56382
2007-01-11
 
46672
2007-01-12
 
45940
2007-01-23
 
43905
2007-01-16
 
42545
Other values (360)
8989143 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2007-01-11
2nd row2007-01-10
3rd row2007-01-10
4th row2007-03-27
5th row2007-01-10

Common Values

ValueCountFrequency (%)
2007-01-1056382
 
0.6%
2007-01-1146672
 
0.5%
2007-01-1245940
 
0.5%
2007-01-2343905
 
0.5%
2007-01-1642545
 
0.5%
2007-01-1741739
 
0.5%
2007-01-1541155
 
0.4%
2007-01-1940698
 
0.4%
2007-01-2240018
 
0.4%
2007-04-1039449
 
0.4%
Other values (355)8786084
95.2%

Length

2022-03-19T15:26:26.176254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2007-01-1056382
 
0.6%
2007-01-1146672
 
0.5%
2007-01-1245940
 
0.5%
2007-01-2343905
 
0.5%
2007-01-1642545
 
0.5%
2007-01-1741739
 
0.5%
2007-01-1541155
 
0.4%
2007-01-1940698
 
0.4%
2007-01-2240018
 
0.4%
2007-04-1039449
 
0.4%
Other values (355)8786084
95.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SALES_MARKET
Real number (ℝ≥0)

HIGH CORRELATION

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.451603946 × 1015
Minimum1.436722689 × 1014
Maximum9.831241332 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.8 MiB
2022-03-19T15:26:26.280272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.436722689 × 1014
5-th percentile1.319526191 × 1015
Q12.460355647 × 1015
median4.676356572 × 1015
Q35.918065357 × 1015
95-th percentile7.445666649 × 1015
Maximum9.831241332 × 1015
Range9.687569063 × 1015
Interquartile range (IQR)3.45770971 × 1015

Descriptive statistics

Standard deviation2.111584968 × 1015
Coefficient of variation (CV)0.4743425053
Kurtosis-0.514131839
Mean4.451603946 × 1015
Median Absolute Deviation (MAD)2.216000925 × 1015
Skewness0.2752812975
Sum1.755584477 × 1018
Variance4.458791079 × 1030
MonotonicityNot monotonic
2022-03-19T15:26:26.417600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.460355647 × 10152323721
25.2%
6.982998843 × 1015956026
 
10.4%
5.492600423 × 1015760762
 
8.2%
3.823004278 × 1015597118
 
6.5%
4.676356572 × 1015335815
 
3.6%
1.319526191 × 1015315543
 
3.4%
5.218436845 × 1015273820
 
3.0%
5.703060121 × 1015272958
 
3.0%
6.745023307 × 1015271118
 
2.9%
4.680091206 × 1015251416
 
2.7%
Other values (62)2866290
31.1%
ValueCountFrequency (%)
1.436722689 × 10143277
 
< 0.1%
2.200278881 × 101442501
 
0.5%
4.272004361 × 101486565
 
0.9%
4.346767962 × 1014101387
 
1.1%
5.522629473 × 101414012
 
0.2%
1.040662998 × 1015593
 
< 0.1%
1.268037904 × 101586422
 
0.9%
1.319526191 × 1015315543
3.4%
1.51438236 × 101553419
 
0.6%
2.099453761 × 101548389
 
0.5%
ValueCountFrequency (%)
9.831241332 × 1015159103
1.7%
9.509246387 × 10154178
 
< 0.1%
9.475875089 × 101515739
 
0.2%
9.306828478 × 101533583
 
0.4%
9.265787999 × 101595950
1.0%
9.172083597 × 1015195
 
< 0.1%
9.071749734 × 101557
 
< 0.1%
8.666019511 × 101514392
 
0.2%
8.642649904 × 1015729
 
< 0.1%
8.557423498 × 101525400
 
0.3%

SALES_CHANNEL
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
AGENTS
6243016 
LOT.COM
2607855 
ATO_CTO
 
194883
CALL CENTER
 
122432
LOT TRAVEL
 
56342

Length

Max length11
Median length6
Mean length6.39460726
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLOT TRAVEL
2nd rowAGENTS
3rd rowLOT.COM
4th rowAGENTS
5th rowAGENTS

Common Values

ValueCountFrequency (%)
AGENTS6243016
67.7%
LOT.COM2607855
28.3%
ATO_CTO194883
 
2.1%
CALL CENTER122432
 
1.3%
LOT TRAVEL56342
 
0.6%
DCS59
 
< 0.1%

Length

2022-03-19T15:26:26.536361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-19T15:26:26.611925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
agents6243016
66.4%
lot.com2607855
27.7%
ato_cto194883
 
2.1%
call122432
 
1.3%
center122432
 
1.3%
lot56342
 
0.6%
travel56342
 
0.6%
dcs59
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TRIP_TYPE
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
ROUND TRIP
6257485 
ONE WAY
1796410 
MULTICITY
1170692 

Length

Max length10
Median length10
Mean length9.288865507
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowROUND TRIP
2nd rowROUND TRIP
3rd rowROUND TRIP
4th rowROUND TRIP
5th rowROUND TRIP

Common Values

ValueCountFrequency (%)
ROUND TRIP6257485
67.8%
ONE WAY1796410
 
19.5%
MULTICITY1170692
 
12.7%

Length

2022-03-19T15:26:26.708944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-19T15:26:26.784940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
round6257485
36.2%
trip6257485
36.2%
one1796410
 
10.4%
way1796410
 
10.4%
multicity1170692
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BOOKING_WINDOW_D
Real number (ℝ)

ZEROS

Distinct365
Distinct (%)< 0.1%
Missing438
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean44.30218668
Minimum-1
Maximum365
Zeros166525
Zeros (%)1.8%
Negative6569
Negative (%)0.1%
Memory size140.8 MiB
2022-03-19T15:26:26.873586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q17
median22
Q358
95-th percentile168
Maximum365
Range366
Interquartile range (IQR)51

Descriptive statistics

Standard deviation56.14017749
Coefficient of variation (CV)1.267210079
Kurtosis4.990335168
Mean44.30218668
Median Absolute Deviation (MAD)17
Skewness2.131762129
Sum408649971
Variance3151.719529
MonotonicityNot monotonic
2022-03-19T15:26:27.002847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1374643
 
4.1%
7309574
 
3.4%
2307512
 
3.3%
5305500
 
3.3%
3298851
 
3.2%
4293911
 
3.2%
6288406
 
3.1%
8224411
 
2.4%
10198027
 
2.1%
11187889
 
2.0%
Other values (355)6435425
69.8%
ValueCountFrequency (%)
-16569
 
0.1%
0166525
1.8%
1374643
4.1%
2307512
3.3%
3298851
3.2%
4293911
3.2%
5305500
3.3%
6288406
3.1%
7309574
3.4%
8224411
2.4%
ValueCountFrequency (%)
36535
 
< 0.1%
36412
 
< 0.1%
361131
< 0.1%
360118
< 0.1%
359175
< 0.1%
358141
< 0.1%
357236
< 0.1%
356173
< 0.1%
355131
< 0.1%
354219
< 0.1%

STAY_LENGTH_D
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct367
Distinct (%)< 0.1%
Missing424
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-1937.802156
Minimum-9999
Maximum366
Zeros321065
Zeros (%)3.5%
Negative1796410
Negative (%)19.5%
Memory size140.8 MiB
2022-03-19T15:26:27.141012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile-9999
Q11
median4
Q39
95-th percentile33
Maximum366
Range10365
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3964.422267
Coefficient of variation (CV)-2.045834377
Kurtosis0.3764763633
Mean-1937.802156
Median Absolute Deviation (MAD)4
Skewness-1.541520623
Sum-1.787460295 × 1010
Variance15716643.91
MonotonicityNot monotonic
2022-03-19T15:26:27.267216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-99991796410
19.5%
2912232
 
9.9%
3911736
 
9.9%
4686238
 
7.4%
1595092
 
6.5%
7435882
 
4.7%
5421307
 
4.6%
6350153
 
3.8%
0321065
 
3.5%
8303715
 
3.3%
Other values (357)2490333
27.0%
ValueCountFrequency (%)
-99991796410
19.5%
0321065
 
3.5%
1595092
 
6.5%
2912232
9.9%
3911736
9.9%
4686238
 
7.4%
5421307
 
4.6%
6350153
 
3.8%
7435882
 
4.7%
8303715
 
3.3%
ValueCountFrequency (%)
3666
 
< 0.1%
36546
< 0.1%
3647
 
< 0.1%
3622
 
< 0.1%
3614
 
< 0.1%
36020
 
< 0.1%
35918
 
< 0.1%
35877
< 0.1%
35714
 
< 0.1%
35644
< 0.1%

BOOKING_LONG_HOUL_FLAG
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 MiB
False
7243975 
True
1980612 
ValueCountFrequency (%)
False7243975
78.5%
True1980612
 
21.5%
2022-03-19T15:26:27.356335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

BOOKING_DOMESTIC_FLAG
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 MiB
False
9223406 
True
 
1181
ValueCountFrequency (%)
False9223406
> 99.9%
True1181
 
< 0.1%
2022-03-19T15:26:27.392185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

FLIGHT_COUPONS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct155
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.950106059
Minimum1
Maximum315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.8 MiB
2022-03-19T15:26:27.471013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q38
95-th percentile20
Maximum315
Range314
Interquartile range (IQR)6

Descriptive statistics

Standard deviation16.47474572
Coefficient of variation (CV)2.072267413
Kurtosis49.09842659
Mean7.950106059
Median Absolute Deviation (MAD)2
Skewness6.224773058
Sum73336445
Variance271.4172464
MonotonicityNot monotonic
2022-03-19T15:26:27.591410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42999775
32.5%
22559332
27.7%
81174648
 
12.7%
1460980
 
5.0%
6428625
 
4.6%
12385880
 
4.2%
16238396
 
2.6%
3205973
 
2.2%
1096752
 
1.0%
2087102
 
0.9%
Other values (145)587124
 
6.4%
ValueCountFrequency (%)
1460980
 
5.0%
22559332
27.7%
3205973
 
2.2%
42999775
32.5%
524073
 
0.3%
6428625
 
4.6%
75114
 
0.1%
81174648
 
12.7%
928140
 
0.3%
1096752
 
1.0%
ValueCountFrequency (%)
315189
 
< 0.1%
279279
 
< 0.1%
268201
 
< 0.1%
265159
 
< 0.1%
248248
 
< 0.1%
235141
 
< 0.1%
232232
 
< 0.1%
222222
 
< 0.1%
220220
 
< 0.1%
216810
< 0.1%

SEGMENTS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.806591124
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.8 MiB
2022-03-19T15:26:27.696308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.458649842
Coefficient of variation (CV)0.2538758416
Kurtosis1.324028627
Mean1.806591124
Median Absolute Deviation (MAD)0
Skewness-0.5650081206
Sum16665057
Variance0.2103596776
MonotonicityNot monotonic
2022-03-19T15:26:27.776789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
26972663
75.6%
12023111
 
21.9%
3219421
 
2.4%
48941
 
0.1%
5311
 
< 0.1%
657
 
< 0.1%
734
 
< 0.1%
1033
 
< 0.1%
816
 
< 0.1%
ValueCountFrequency (%)
12023111
 
21.9%
26972663
75.6%
3219421
 
2.4%
48941
 
0.1%
5311
 
< 0.1%
657
 
< 0.1%
734
 
< 0.1%
816
 
< 0.1%
1033
 
< 0.1%
ValueCountFrequency (%)
1033
 
< 0.1%
816
 
< 0.1%
734
 
< 0.1%
657
 
< 0.1%
5311
 
< 0.1%
48941
 
0.1%
3219421
 
2.4%
26972663
75.6%
12023111
 
21.9%

PAX_N
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct90
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.91961342
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.8 MiB
2022-03-19T15:26:27.879512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile8
Maximum99
Range98
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.056848711
Coefficient of variation (CV)2.074537906
Kurtosis45.7372056
Mean2.91961342
Median Absolute Deviation (MAD)0
Skewness6.115811801
Sum26932228
Variance36.68541631
MonotonicityNot monotonic
2022-03-19T15:26:28.002774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15034103
54.6%
22129340
23.1%
3725200
 
7.9%
4514854
 
5.6%
5191594
 
2.1%
6105102
 
1.1%
755056
 
0.6%
847960
 
0.5%
947758
 
0.5%
3018156
 
0.2%
Other values (80)355464
 
3.9%
ValueCountFrequency (%)
15034103
54.6%
22129340
23.1%
3725200
 
7.9%
4514854
 
5.6%
5191594
 
2.1%
6105102
 
1.1%
755056
 
0.6%
847960
 
0.5%
947758
 
0.5%
1016692
 
0.2%
ValueCountFrequency (%)
99297
< 0.1%
9898
 
< 0.1%
9696
 
< 0.1%
92279
< 0.1%
90360
< 0.1%
8989
 
< 0.1%
88176
< 0.1%
86258
< 0.1%
85170
< 0.1%
83166
< 0.1%

INTINERARY
Categorical

HIGH CARDINALITY

Distinct55794
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
XMK-OUO-XMK
 
77197
OUO-FBI-OUO
 
72312
OUO-UGK-OUO
 
67890
OUO-WXA-OUO
 
66542
LFN-OUO-LFN
 
61912
Other values (55789)
8878734 

Length

Max length35
Median length11
Mean length14.47592906
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowOUO-WXA-OUO
2nd rowOUO-RRS-OUO
3rd rowHIX-OUO-HIX
4th rowDZN-OUO-LPF-OUO-EVJ
5th rowNTH-LFF-NTH

Common Values

ValueCountFrequency (%)
XMK-OUO-XMK77197
 
0.8%
OUO-FBI-OUO72312
 
0.8%
OUO-UGK-OUO67890
 
0.7%
OUO-WXA-OUO66542
 
0.7%
LFN-OUO-LFN61912
 
0.7%
OUO-VWT-OUO55118
 
0.6%
FBI-OUO-FBI49108
 
0.5%
OUO-VIM-OUO43878
 
0.5%
OUO-VMX-OUO40511
 
0.4%
OUO-GMW-OUO37334
 
0.4%
Other values (55784)8652785
93.8%

Length

2022-03-19T15:26:28.137048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xmk-ouo-xmk77197
 
0.8%
ouo-fbi-ouo72312
 
0.8%
ouo-ugk-ouo67890
 
0.7%
ouo-wxa-ouo66542
 
0.7%
lfn-ouo-lfn61912
 
0.7%
ouo-vwt-ouo55118
 
0.6%
fbi-ouo-fbi49108
 
0.5%
ouo-vim-ouo43878
 
0.5%
ouo-vmx-ouo40511
 
0.4%
ouo-gmw-ouo37334
 
0.4%
Other values (55784)8652785
93.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BOOKING_ORIGIN_AIRPORT
Categorical

HIGH CARDINALITY

Distinct145
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
OUO
1866644 
LFF
 
455128
EIE
 
278176
HIX
 
236747
NTH
 
217121
Other values (140)
6170771 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOUO
2nd rowOUO
3rd rowHIX
4th rowDZN
5th rowNTH

Common Values

ValueCountFrequency (%)
OUO1866644
 
20.2%
LFF455128
 
4.9%
EIE278176
 
3.0%
HIX236747
 
2.6%
NTH217121
 
2.4%
LFN212850
 
2.3%
CYE201002
 
2.2%
FBI195876
 
2.1%
XMK193526
 
2.1%
UGK182378
 
2.0%
Other values (135)5185139
56.2%

Length

2022-03-19T15:26:28.227558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ouo1866644
 
20.2%
lff455128
 
4.9%
eie278176
 
3.0%
hix236747
 
2.6%
nth217121
 
2.4%
lfn212850
 
2.3%
cye201002
 
2.2%
fbi195876
 
2.1%
xmk193526
 
2.1%
ugk182378
 
2.0%
Other values (135)5185139
56.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BOOKING_ORIGIN_COUNTRY_CODE
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size140.8 MiB
PL
2901845 
DE
832391 
US
591713 
UA
468357 
EE
455128 
Other values (46)
3975150 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPL
2nd rowPL
3rd rowUS
4th rowUA
5th rowLT

Common Values

ValueCountFrequency (%)
PL2901845
31.5%
DE832391
 
9.0%
US591713
 
6.4%
UA468357
 
5.1%
EE455128
 
4.9%
SE249853
 
2.7%
LT244425
 
2.6%
NL216992
 
2.4%
IL212850
 
2.3%
FR206603
 
2.2%
Other values (41)2844427
30.8%

Length

2022-03-19T15:26:28.311315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pl2901845
31.5%
de832391
 
9.0%
us591713
 
6.4%
ua468357
 
5.1%
ee455128
 
4.9%
se249853
 
2.7%
lt244425
 
2.6%
nl216992
 
2.4%
il212850
 
2.3%
fr206603
 
2.2%
Other values (41)2844427
30.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BOOKING_DEPARTURE_TIME_UTC
Categorical

HIGH CARDINALITY

Distinct83698
Distinct (%)0.9%
Missing438
Missing (%)< 0.1%
Memory size140.8 MiB
2007-08-03 02:05:00
 
1910
2007-07-15 17:50:00
 
1867
2007-07-09 02:05:00
 
1712
2007-06-28 02:05:00
 
1683
2007-04-30 02:05:00
 
1614
Other values (83693)
9215363 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1345 ?
Unique (%)< 0.1%

Sample

1st row2007-05-12 05:25:00
2nd row2007-08-26 11:55:00
3rd row2007-03-06 03:35:00
4th row2007-04-08 03:05:00
5th row2007-01-16 17:35:00

Common Values

ValueCountFrequency (%)
2007-08-03 02:05:001910
 
< 0.1%
2007-07-15 17:50:001867
 
< 0.1%
2007-07-09 02:05:001712
 
< 0.1%
2007-06-28 02:05:001683
 
< 0.1%
2007-04-30 02:05:001614
 
< 0.1%
2007-07-21 02:05:001539
 
< 0.1%
2007-09-21 02:05:001513
 
< 0.1%
2007-07-05 02:05:001487
 
< 0.1%
2007-12-21 09:25:001479
 
< 0.1%
2007-07-13 08:25:001478
 
< 0.1%
Other values (83688)9207867
99.8%

Length

2022-03-19T15:26:28.419525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
08:25:00199168
 
1.1%
02:05:00176140
 
1.0%
17:50:00149657
 
0.8%
12:55:00147078
 
0.8%
05:00:00140003
 
0.8%
05:30:00124182
 
0.7%
01:55:00123850
 
0.7%
08:45:00119544
 
0.6%
08:30:00115082
 
0.6%
05:20:00106939
 
0.6%
Other values (1117)17046655
92.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BOOKING_DESTINATION_AIRPORT
Categorical

HIGH CARDINALITY
MISSING

Distinct147
Distinct (%)< 0.1%
Missing1170692
Missing (%)12.7%
Memory size140.8 MiB
OUO
1257167 
EIE
 
298029
LFF
 
278498
UGK
 
202817
WXA
 
182412
Other values (142)
5834972 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWXA
2nd rowRRS
3rd rowOUO
4th rowLPF
5th rowLFF

Common Values

ValueCountFrequency (%)
OUO1257167
 
13.6%
EIE298029
 
3.2%
LFF278498
 
3.0%
UGK202817
 
2.2%
WXA182412
 
2.0%
FBI176627
 
1.9%
NTH175712
 
1.9%
GMW169386
 
1.8%
FOH168418
 
1.8%
LFN166213
 
1.8%
Other values (137)4978616
54.0%
(Missing)1170692
 
12.7%

Length

2022-03-19T15:26:28.506918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ouo1257167
 
15.6%
eie298029
 
3.7%
lff278498
 
3.5%
ugk202817
 
2.5%
wxa182412
 
2.3%
fbi176627
 
2.2%
nth175712
 
2.2%
gmw169386
 
2.1%
foh168418
 
2.1%
lfn166213
 
2.1%
Other values (137)4978616
61.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BOOKING_DESTINATION_COUNTRY_CODE
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct50
Distinct (%)< 0.1%
Missing1170692
Missing (%)12.7%
Memory size140.8 MiB
PL
2214715 
DE
612387 
UA
492939 
US
405214 
EE
 
278564
Other values (45)
4050076 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFR
2nd rowCA
3rd rowPL
4th rowDE
5th rowEE

Common Values

ValueCountFrequency (%)
PL2214715
24.0%
DE612387
 
6.6%
UA492939
 
5.3%
US405214
 
4.4%
EE278564
 
3.0%
FR240963
 
2.6%
NL212840
 
2.3%
RU202213
 
2.2%
LT200527
 
2.2%
GB179586
 
1.9%
Other values (40)3013947
32.7%
(Missing)1170692
 
12.7%

Length

2022-03-19T15:26:28.595377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pl2214715
27.5%
de612387
 
7.6%
ua492939
 
6.1%
us405214
 
5.0%
ee278564
 
3.5%
fr240963
 
3.0%
nl212840
 
2.6%
ru202213
 
2.5%
lt200527
 
2.5%
gb179586
 
2.2%
Other values (40)3013947
37.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BOOKING_ARRIVAL_TIME_UTC
Categorical

HIGH CARDINALITY

Distinct86418
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size140.8 MiB
2008-01-03 18:05:00
 
1648
2008-01-06 18:05:00
 
1625
2007-09-09 00:10:00
 
1610
2007-11-04 18:05:00
 
1608
2007-09-01 00:10:00
 
1586
Other values (86413)
9216510 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1313 ?
Unique (%)< 0.1%

Sample

1st row2007-05-16 19:55:00
2nd row2007-09-16 10:40:00
3rd row2007-03-21 01:50:00
4th row2007-04-15 14:15:00
5th row2007-01-17 17:05:00

Common Values

ValueCountFrequency (%)
2008-01-03 18:05:001648
 
< 0.1%
2008-01-06 18:05:001625
 
< 0.1%
2007-09-09 00:10:001610
 
< 0.1%
2007-11-04 18:05:001608
 
< 0.1%
2007-09-01 00:10:001586
 
< 0.1%
2008-01-02 18:05:001573
 
< 0.1%
2008-01-01 18:05:001543
 
< 0.1%
2007-12-30 18:05:001517
 
< 0.1%
2007-09-16 00:10:001495
 
< 0.1%
2008-01-04 18:05:001477
 
< 0.1%
Other values (86408)9208905
99.8%

Length

2022-03-19T15:26:28.703365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:10:00198492
 
1.1%
19:40:00180242
 
1.0%
17:05:00142496
 
0.8%
14:15:00132105
 
0.7%
17:00:00125269
 
0.7%
16:55:00122411
 
0.7%
21:45:00121380
 
0.7%
16:50:00120447
 
0.7%
20:45:00119926
 
0.7%
20:55:00118994
 
0.6%
Other values (1202)17067412
92.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-19T15:21:29.007734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:11:06.657035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:12:14.501736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:13:17.335877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:14:10.313097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:14:54.831238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:15:50.527692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:16:34.277939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:17:18.675571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:02.094255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:13.899014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:55.350971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:19:36.243512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:20:12.439873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:20:50.157888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:21:31.629254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:11:09.850214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:12:18.081816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:13:21.548607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:14:13.509070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:14:59.528440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:15:53.558186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:16:37.360801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:17:21.973307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:02.765916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:16.981346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:58.441653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:19:38.924148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:20:14.959114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:20:53.655869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:21:34.243506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:11:12.826207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:12:21.750576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:13:25.270549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:14:16.520746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:15:03.808527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:15:56.461245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:16:40.351207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:17:25.155222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:03.391081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:20.286013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:19:01.471937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:19:41.495768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:20:17.455626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:20:56.398102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:21:37.083537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:11:16.206348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:12:25.799468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:13:29.252874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:14:19.432469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:15:07.728100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:15:59.395371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:16:43.317305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:17:28.522118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:04.016129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:23.168255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:19:04.489817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:19:43.955592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:20:19.909129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:20:58.944105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:21:39.896308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:11:19.919099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:12:30.333310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:13:33.379561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:14:22.464618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:15:11.734373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:16:02.418921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:16:46.388079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:17:31.750489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:04.671183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:26.031570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:19:07.519153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:19:46.614409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:20:22.449923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:21:01.811950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:21:42.416406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:11:23.313377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:12:34.774596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:13:37.461387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:14:25.402292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:15:15.644371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:16:05.494877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:16:49.414371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:17:34.892922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:05.293033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:18:28.814624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:19:10.350528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:19:49.037359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:20:24.844487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:21:04.542576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:21:44.917065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-19T15:11:26.968718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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Correlations

2022-03-19T15:26:28.803385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-19T15:26:28.994589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-19T15:26:29.192258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-19T15:26:29.398787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-19T15:26:29.653445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-19T15:22:18.115862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-19T15:23:14.229379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-19T15:25:11.329049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-19T15:25:28.971023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

TICKET_NUMBERCOUPON_NUMBERORIGIN_AIRPORT_CODEDESTINATION_AIRPORT_CODEFLIGHT_DATE_LOCALTIME_DEPARTURE_LOCAL_TIMEFLIGHT_DISTANCEFLIGHT_RANGEMARKETING_CARRIEROPERATIONAL_CARRIERBOOKED_CLASSBOOKED_CABINAIRCRAFT_TYPEFARE_BASISVABUPGRADED_FLAGUPGRADE_TYPEUPGRADE_SALES_DATEBOOKING_IDORIGINAL_TICKET_NUMBERFORM_OF_PAYMENTCURRENCYTOTAL_PRICETOTAL_PRICE_PLNPAX_GENDERPAX_TYPECORPORATE_CONTRACT_FLGLOYAL_CUSTOMERLOYAL_CUSTOMER_IDLOYAL_CUSTOMER_DATE_OF_BIRTHLOYAL_CUSTOMER_REGISTERED_DATESALES_DATESALES_MARKETSALES_CHANNELTRIP_TYPEBOOKING_WINDOW_DSTAY_LENGTH_DBOOKING_LONG_HOUL_FLAGBOOKING_DOMESTIC_FLAGFLIGHT_COUPONSSEGMENTSPAX_NINTINERARYBOOKING_ORIGIN_AIRPORTBOOKING_ORIGIN_COUNTRY_CODEBOOKING_DEPARTURE_TIME_UTCBOOKING_DESTINATION_AIRPORTBOOKING_DESTINATION_COUNTRY_CODEBOOKING_ARRIVAL_TIME_UTC
086044248657480572WXAOUO2007-05-1619:40:001344SHORT-HAUL24346152054896832434615205489683OEconomy738OZSTDJ0STANDARDNNaNNaN95923745342479389379265972706784CAPLN620.35620.35MADULTNNNaNNaNNaN2007-01-112460355646543430LOT TRAVELROUND TRIP121.04.0NN221OUO-WXA-OUOOUOPL2007-05-12 05:25:00WXAFR2007-05-16 19:55:00
190183018139901251OUORRS2007-08-2613:55:006942LONG-HAUL24346152054896832434615205489683UEconomy788UKPRO10BASICNNaNNaN48122697837569309379265972706784CAEUR514.972151.49MADULTNNNaNNaNNaN2007-01-105703060120714659AGENTSROUND TRIP228.021.0YN824OUO-RRS-OUOOUOPL2007-08-26 11:55:00RRSCA2007-09-16 10:40:00
228225104877227802OUOHIX2007-03-2016:40:007521LONG-HAUL24346152054896832434615205489683UEconomy788ULPRO11BASICNNaNNaN63664258060506649379265972706784CCVI4USD648.772270.18FADULTNNNaNNaNNaN2007-01-106982998843464221LOT.COMROUND TRIP55.015.0YN221HIX-OUO-HIXHIXUS2007-03-06 03:35:00OUOPL2007-03-21 01:50:00
325499441567000911DZNOUO2007-04-0806:05:00721SHORT-HAUL24346152054896832434615205489683WEconomyE75WSTDK0STANDARDNNaNNaN9287887639832909379265972706784CAUAH8445.001096.16FADULTNNNaNNaNNaN2007-03-271319526191137192AGENTSROUND TRIP12.07.0NN421DZN-OUO-LPF-OUO-EVJDZNUA2007-04-08 03:05:00LPFDE2007-04-15 14:15:00
411902175394846802LFFNTH2007-01-1718:05:00522SHORT-HAUL24346152054896832434615205489683WEconomyCRNWCSERT/UD10OTHERNNaNNaN78086808435061609379265972706784CAEUR180.03752.15MADULTNNNaNNaNNaN2007-01-104311501618921268AGENTSROUND TRIP6.01.0NN221NTH-LFF-NTHNTHLT2007-01-16 17:35:00LFFEE2007-01-17 17:05:00
52241452141357102OUOSIB2007-05-1317:00:001038SHORT-HAUL24346152054896832434615205489683VEconomy73CVSAVM0SAVERNNaNNaN57005178019040849379265972706784CACHF309.501102.53MADULTNNNaNNaNNaN2007-01-117445666648510309AGENTSROUND TRIP119.03.0NN824SIB-OUO-SIBSIBCH2007-05-10 08:25:00OUOPL2007-05-13 17:05:00
626722324509157434OUOUIT2007-04-0816:55:00818SHORT-HAUL24346152054896832434615205489683ZBusinessCRNZSPCOTHERNNaNNaN69067822153303869379265972706784CCAX3EUR300.331254.75MADULTNNNaNNaNNaN2007-01-104656329746243382AGENTSROUND TRIP81.07.0NN1223UIT-OUO-NWZ-OUO-UITUITSE2007-04-01 05:55:00NWZIT2007-04-08 16:45:00
787900405388254761OUOVMX2007-04-2719:45:00537SHORT-HAUL24346152054896832434615205489683OEconomyDH4OZSAVJ0SAVERNNaNNaN35337509863301329379265972706784CCCA5PLN350.00350.00FADULTNY5.503844e+151961-03-261996-06-132007-01-112460355646543430LOT.COMROUND TRIP106.010.0NN623OUO-VMX-OUOOUOPL2007-04-27 17:45:00VMXHU2007-05-07 06:20:00
817937554008655693OUOHIX2007-03-2916:45:007521LONG-HAUL24346152054896832434615205489683HEconomy788HLXINK/UD06STANDARDNNaNNaN90013486332535319379265972706784CCVI4USD1300.814457.10MADULTNNNaNNaNNaN2007-03-236982998843464221AGENTSROUND TRIP3.04.0YN321HIX-EIE-OUO-HIXHIXUS2007-03-26 02:30:00EIEPL2007-03-30 00:35:00
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Last rows

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922457837105599737466072FZXLFF2007-02-0500:10:00313SHORT-HAUL24346152054896834676356571680678LEconomyAT7LCSERTOTHERNNaNNaN85909474272697589379265972706784CARUB16342.00975.62MADULTNNNaNNaNNaN2007-01-295218436844853814AGENTSROUND TRIP4.02.0NN221LFF-FZX-LFFLFFEE2007-02-02 19:35:00FZXRU2007-02-04 22:10:00
922457918966748739915221CYEOUO2007-07-2722:00:006863LONG-HAUL24346152054896832434615205489683VEconomy789VHWTANFLEXNNaNNaN64821985669337469379265972706784CCCA5USD1296.424619.66FADULTNNNaNNaNNaN2007-05-086982998843464221LOT.COMROUND TRIP81.023.0YN824CYE-OUO-CYECYEUS2007-07-28 02:00:00OUOPL2007-08-20 00:05:00
922458057888374303841661OUOUGK2007-08-1516:45:001102SHORT-HAUL24346152054896832434615205489683WEconomy73HWSTDI3STANDARDNNaNNaN33923222985078189379265972706784DOTPAPLN759.47759.47FADULTNNNaNNaNNaN2007-05-102460355646543430LOT.COMROUND TRIP97.03.0NN623OUO-UGK-OUOOUOPL2007-08-15 14:45:00UGKNL2007-08-18 19:40:00
922458138789783393275913EDNOUO2007-09-0714:45:00819SHORT-HAUL24346152054896832434615205489683WEconomyDH4WHWTAN/IN90OTHERNNaNNaN10669503083318399379265972706784CCVI4USD140.81501.76MINFANTNNNaNNaNNaN2007-05-086982998843464221LOT.COMMULTICITY76.047.0YN1223HIX-OUO-EDN-OUO-HIXOUOPL2007-07-23 09:20:00NaNNaN2007-09-08 00:35:00
922458210966324978845101LPFOUO2007-02-0519:30:00523SHORT-HAUL24346152054896832434615205489683VEconomyDH4V1STD0STANDARDNNaNNaN66670250840075509379265972706784CAUAH6803.00813.64MADULTNNNaNNaNNaN2007-02-051319526191137192AGENTSONE WAY0.0-9999.0NN211LPF-OUO-DZNLPFDE2007-02-05 18:30:00DZNUA2007-02-05 23:10:00
922458334830704398185542KHYOUO2007-08-0417:55:001070SHORT-HAUL24346152054896832434615205489683UEconomyE75USAVJ21SAVERNNaNNaN84435107844846819379265972706784DOTPAPLN675.51675.51MADULTNNNaNNaNNaN2007-04-022460355646543430LOT.COMROUND TRIP116.08.0NN623OUO-KHY-OUOOUOPL2007-07-27 08:55:00KHYBG2007-08-04 16:55:00
922458499762886263377191BUGOUO2007-06-0207:00:00753SHORT-HAUL24346152054896832434615205489683KEconomyDH4K1FLX0FLEXNNaNNaN61211708024220769379265972706784CACHF398.001408.80MADULTNNNaNNaNNaN2007-04-277445666648510309AGENTSONE WAY36.0-9999.0NN211BUG-OUO-KNHBUGDE2007-06-02 05:00:00KNHRU2007-06-02 10:50:00
922458545528960021368371HLJOUO2007-09-1618:00:00940SHORT-HAUL24346152054896832434615205489683SEconomyE75SSAVJ14SAVERNNaNNaN41199974021180939379265972706784CARUB20715.001157.97MADULTNNNaNNaNNaN2007-08-105218436844853814AGENTSROUND TRIP37.03.0NN623HLJ-OUO-HLJHLJRO2007-09-16 15:00:00OUOPL2007-09-19 22:25:00
922458684406482410698031OUOGPJ2007-05-1616:50:001138SHORT-HAUL24346152054896832434615205489683KEconomyE95K1STD9STANDARDNNaNNaN16536360046491109379265972706784CCAX3EUR300.211284.03MADULTNNNaNNaNNaN2007-05-074656329746243382AGENTSONE WAY9.0-9999.0NN111OUO-GPJOUOPL2007-05-16 14:50:00GPJIT2007-05-16 17:00:00